Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray');

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'));

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_inputs = tf.placeholder(tf.float32, 
                                 (None, image_width, image_height, image_channels),
                                'real_inputs')
    
    z_inputs = tf.placeholder(tf.float32,
                              (None, z_dim),
                              'z_inputs')
    
    learning_rate = tf.placeholder(tf.float32, 
                                   name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False,dropout_rate=0.4, alpha = 0.1):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    def dropout(x):
        #Borrows keep prob from default kw param
        return tf.layers.dropout(inputs=x, rate=dropout_rate,training=True)
    
    # added xavier initializer, an added layer and dropouts per REVIEW
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        layer0 = dropout(tf.layers.conv2d(images, 32, 5, strides=2, padding='same',
                              kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d()))
        
        relu0 = tf.maximum(alpha * layer0, layer0)
        
        
        
        layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn1 = dropout(tf.layers.batch_normalization(layer1, training=True)) #also implementing dropout
        relu1 = tf.maximum(alpha * layer1, layer1)
        
        
        
        layer2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = dropout(tf.layers.batch_normalization(layer2, training=True))
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        
        layer3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='valid',
                                 kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = dropout(tf.layers.batch_normalization(layer3, training=True))
        relu3 = tf.maximum(alpha * bn3, bn3)
        
    
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, dropout_rate=0.2, alpha = 0.1):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """ 
    # added xavier initializer, an added layer and dropouts per REVIEW
    
    with tf.variable_scope('generator', reuse=(not is_train)):
        # Fully connected Layer
        layer0 = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start the convolutional stack
        layer0 = tf.reshape(layer0, (-1, 7, 7, 512))
        layer0 = tf.layers.batch_normalization(layer0, training= is_train)
        layer0 = tf.maximum(alpha * layer0, layer0)
        
       
        layer1 = tf.layers.conv2d_transpose(layer0, 128, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        layer1 = tf.layers.batch_normalization(layer1, training=is_train)
        #Per Review
        layer1 = tf.layers.dropout(inputs=layer1, rate=dropout_rate, training= is_train)
        layer1 = tf.maximum(alpha * layer1, layer1)
        
        
        layer2 = tf.layers.conv2d_transpose(layer1, 64, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        layer2 = tf.layers.batch_normalization(layer2, training= is_train)
        #Per Review
        layer2 = tf.layers.dropout(inputs=layer2, rate=dropout_rate, training= is_train)
        layer2 = tf.maximum(alpha * layer2, layer2)
    
        
        
        layer3 = tf.layers.conv2d_transpose(layer2, 32, 5, strides=1, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        layer3 = tf.layers.batch_normalization(layer3, training= is_train)
        #Per Review
        layer3 = tf.layers.dropout(inputs=layer3, rate=dropout_rate, training= is_train)
        layer3 = tf.maximum(alpha * layer3, layer3)
        
        
        # Output layer
        
        logits = tf.layers.conv2d_transpose(layer3, out_channel_dim,3, strides=1, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    # Added a smoothing factor per REVIEW 
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real)*.9)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
        logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    d_updates = [opt for opt in update_ops if opt.name.startswith('discriminator')]
    g_updates = [opt for opt in update_ops if opt.name.startswith('generator')]

    with tf.control_dependencies(d_updates):
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)

    with tf.control_dependencies(g_updates):
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
            
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    saver = tf.train.Saver(var_list=g_vars)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            batches = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                resc_batch_images = 2*batch_images ### rescaling to match tanh output 
                
                train_d = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                train_g = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                train_gxtra = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                #train_g2xtra = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})

                batches +=1
                
                if batches % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(batches),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)        

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [15]:
batch_size = 128
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 100... Discriminator Loss: 0.9807... Generator Loss: 1.1609
Epoch 1/2... Batch 200... Discriminator Loss: 1.3023... Generator Loss: 5.0855
Epoch 1/2... Batch 300... Discriminator Loss: 0.7556... Generator Loss: 1.8875
Epoch 1/2... Batch 400... Discriminator Loss: 1.0899... Generator Loss: 1.1365
Epoch 2/2... Batch 100... Discriminator Loss: 1.6581... Generator Loss: 0.4640
Epoch 2/2... Batch 200... Discriminator Loss: 0.8736... Generator Loss: 1.6812
Epoch 2/2... Batch 300... Discriminator Loss: 1.3321... Generator Loss: 0.8731
Epoch 2/2... Batch 400... Discriminator Loss: 0.7853... Generator Loss: 1.6896

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 100... Discriminator Loss: 0.7819... Generator Loss: 2.1766
Epoch 1/1... Batch 200... Discriminator Loss: 0.4241... Generator Loss: 3.5353
Epoch 1/1... Batch 300... Discriminator Loss: 3.1353... Generator Loss: 0.4014
Epoch 1/1... Batch 400... Discriminator Loss: 0.5787... Generator Loss: 3.3356
Epoch 1/1... Batch 500... Discriminator Loss: 0.5350... Generator Loss: 1.9385
Epoch 1/1... Batch 600... Discriminator Loss: 1.0475... Generator Loss: 1.0744
Epoch 1/1... Batch 700... Discriminator Loss: 1.1469... Generator Loss: 0.9855
Epoch 1/1... Batch 800... Discriminator Loss: 1.0046... Generator Loss: 0.6379
Epoch 1/1... Batch 900... Discriminator Loss: 2.7112... Generator Loss: 0.4135
Epoch 1/1... Batch 1000... Discriminator Loss: 1.5292... Generator Loss: 0.5489
Epoch 1/1... Batch 1100... Discriminator Loss: 1.6754... Generator Loss: 0.7282
Epoch 1/1... Batch 1200... Discriminator Loss: 1.6226... Generator Loss: 0.6703
Epoch 1/1... Batch 1300... Discriminator Loss: 1.4944... Generator Loss: 0.7836
Epoch 1/1... Batch 1400... Discriminator Loss: 1.5098... Generator Loss: 0.9950
Epoch 1/1... Batch 1500... Discriminator Loss: 1.7361... Generator Loss: 0.8638
Epoch 1/1... Batch 1600... Discriminator Loss: 1.6108... Generator Loss: 0.5122
Epoch 1/1... Batch 1700... Discriminator Loss: 1.5949... Generator Loss: 0.9724
Epoch 1/1... Batch 1800... Discriminator Loss: 1.7203... Generator Loss: 0.4641
Epoch 1/1... Batch 1900... Discriminator Loss: 1.2678... Generator Loss: 0.7230
Epoch 1/1... Batch 2000... Discriminator Loss: 1.5317... Generator Loss: 0.5573
Epoch 1/1... Batch 2100... Discriminator Loss: 1.5142... Generator Loss: 0.5654
Epoch 1/1... Batch 2200... Discriminator Loss: 1.7316... Generator Loss: 0.6170
Epoch 1/1... Batch 2300... Discriminator Loss: 1.6329... Generator Loss: 0.6198
Epoch 1/1... Batch 2400... Discriminator Loss: 1.6591... Generator Loss: 0.7492
Epoch 1/1... Batch 2500... Discriminator Loss: 1.6853... Generator Loss: 0.6593
Epoch 1/1... Batch 2600... Discriminator Loss: 1.9054... Generator Loss: 0.5475
Epoch 1/1... Batch 2700... Discriminator Loss: 1.3602... Generator Loss: 0.6294
Epoch 1/1... Batch 2800... Discriminator Loss: 1.3763... Generator Loss: 0.7315
Epoch 1/1... Batch 2900... Discriminator Loss: 1.4514... Generator Loss: 0.5980
Epoch 1/1... Batch 3000... Discriminator Loss: 1.7233... Generator Loss: 0.5094
Epoch 1/1... Batch 3100... Discriminator Loss: 1.3643... Generator Loss: 0.6660
Epoch 1/1... Batch 3200... Discriminator Loss: 1.4415... Generator Loss: 0.6691
Epoch 1/1... Batch 3300... Discriminator Loss: 1.3046... Generator Loss: 0.7023
Epoch 1/1... Batch 3400... Discriminator Loss: 1.3841... Generator Loss: 0.5805
Epoch 1/1... Batch 3500... Discriminator Loss: 1.4823... Generator Loss: 0.5748
Epoch 1/1... Batch 3600... Discriminator Loss: 1.5796... Generator Loss: 0.6891
Epoch 1/1... Batch 3700... Discriminator Loss: 1.3659... Generator Loss: 0.6348
Epoch 1/1... Batch 3800... Discriminator Loss: 1.7739... Generator Loss: 0.5864
Epoch 1/1... Batch 3900... Discriminator Loss: 1.7994... Generator Loss: 0.5758
Epoch 1/1... Batch 4000... Discriminator Loss: 1.7018... Generator Loss: 0.5620
Epoch 1/1... Batch 4100... Discriminator Loss: 1.4775... Generator Loss: 0.6207
Epoch 1/1... Batch 4200... Discriminator Loss: 1.4901... Generator Loss: 0.6031
Epoch 1/1... Batch 4300... Discriminator Loss: 1.3048... Generator Loss: 0.6868
Epoch 1/1... Batch 4400... Discriminator Loss: 1.5219... Generator Loss: 0.6380
Epoch 1/1... Batch 4500... Discriminator Loss: 1.6052... Generator Loss: 0.7572
Epoch 1/1... Batch 4600... Discriminator Loss: 1.7105... Generator Loss: 0.7418
Epoch 1/1... Batch 4700... Discriminator Loss: 1.4614... Generator Loss: 0.6733
Epoch 1/1... Batch 4800... Discriminator Loss: 1.7650... Generator Loss: 0.6974
Epoch 1/1... Batch 4900... Discriminator Loss: 1.5198... Generator Loss: 0.7426
Epoch 1/1... Batch 5000... Discriminator Loss: 1.4727... Generator Loss: 0.7436
Epoch 1/1... Batch 5100... Discriminator Loss: 1.5231... Generator Loss: 0.6987
Epoch 1/1... Batch 5200... Discriminator Loss: 1.3476... Generator Loss: 0.7340
Epoch 1/1... Batch 5300... Discriminator Loss: 1.3816... Generator Loss: 0.6847
Epoch 1/1... Batch 5400... Discriminator Loss: 1.7243... Generator Loss: 0.5970
Epoch 1/1... Batch 5500... Discriminator Loss: 1.5496... Generator Loss: 0.6788
Epoch 1/1... Batch 5600... Discriminator Loss: 1.5317... Generator Loss: 0.6439
Epoch 1/1... Batch 5700... Discriminator Loss: 1.5070... Generator Loss: 0.6070
Epoch 1/1... Batch 5800... Discriminator Loss: 1.5089... Generator Loss: 0.7065
Epoch 1/1... Batch 5900... Discriminator Loss: 1.5106... Generator Loss: 0.6311
Epoch 1/1... Batch 6000... Discriminator Loss: 1.5061... Generator Loss: 0.5945
Epoch 1/1... Batch 6100... Discriminator Loss: 1.4240... Generator Loss: 0.7146
Epoch 1/1... Batch 6200... Discriminator Loss: 1.3199... Generator Loss: 0.6478
Epoch 1/1... Batch 6300... Discriminator Loss: 1.6312... Generator Loss: 0.7781

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.